1. Field
Embodiments relate to image detection and satellite and aerial imagery.
2. Related Art
Detecting trees from above the Earth's surface is increasingly important in geographical information systems, models and applications. Depending on their input data, three methods of tree detection include LiDAR-based, NIR-based, and image-based methods. Light Detection and Ranging (LiDAR) and Near Infrared (NIR) are remote sensing technologies which provide geometric and radiometric measures on the Earth surface. Given that most trees fall into a small range on both measures, LiDAR and NIR data provide a strong heuristic on tree detection. However, the availability of remote sensing imagery is very limited compared to aerial imagery. On the other hand, significant progress has been made on image-based object recognition in the computer vision community.
Current methods of object detection from aerial imagery propose image-based features for extracting roads, intersections, buildings and compound objects such as harbors from aerial imagery. However, such man-made objects have a defined shapes and recurrent patterns, so the features cannot be directly applied to tree detection. Tree detection is necessary to find out where to place tree models in a geographical model or geographical information system (GIS).
Some tree detection methods propose a method for parsing aerial imagery into an object hierarchy. An aerial image is learned at both scene and object levels with color histogram and a bag of scale-invariant feature transform (SIFT) features. Contextual constraints are then applied to resolve the ambiguities of learned results (e.g., cars on top of trees). However, since the object inference is learned in the context of multiple objects, the discriminating power is lowered. The final results may contain many false positives (about 20% for trees), even after contextual constraints are applied.